Python时间序列LSTM预测系列教程（9）-多变量

多变量LSTM预测模型（3）

教程原文链接

前置教程：

Python时间序列LSTM预测系列教程（7）-多变量

Python时间序列LSTM预测系列教程（8）-多变量

定义&训练模型

1、数据划分成训练和测试数据

本教程用第一年数据做训练，剩余4年数据做评估

2、输入=1时间步长，8个feature

3、第一层隐藏层节点=50，输出节点=1

4、用平均绝对误差MAE做损失函数、Adam的随机梯度下降做优化

5、epoch=50, batch_size=72

模型评估

1、预测后需要做逆缩放

2、用RMSE做评估

代码解析

# coding=utf-8                                                                                                                                                                                   from math import sqrtfrom numpy import concatenatefrom matplotlib import pyplotfrom pandas import read_csvfrom pandas import DataFramefrom pandas import concatfrom sklearn.preprocessing import MinMaxScalerfrom sklearn.preprocessing import LabelEncoderfrom sklearn.metrics import mean_squared_errorfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import LSTM #转成有监督数据def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):    n_vars = 1 if type(data) is list else data.shape[1]    df = DataFrame(data)    cols, names = list(), list()    #数据序列(也将就是input)    for i in range(n_in, 0, -1):        cols.append(df.shift(i))        names+=[('var%d(t-%d)'%(j+1, i)) for j in range(n_vars)]    #预测数据（input对应的输出值）    for i in range(0, n_out, 1):        cols.append(df.shift(-i))        if i==0:            names+=[('var%d(t)'%(j+1)) for j in range(n_vars)]        else:            names+=[('var%d(t+%d))'%(j+1, i)) for j in range(n_vars)]    #拼接    agg = concat(cols, axis=1)    if dropnan:        agg.dropna(inplace=True)    return agg #数据预处理#--------------------------dataset = read_csv('data_set/air_pollution_new.csv', header=0, index_col=0)values = dataset.values #标签编码encoder = LabelEncoder()values[:,4] = encoder.fit_transform(values[:,4])#保证为floatvalues = values.astype('float32')#归一化 scaler = MinMaxScaler(feature_range=(0,1))scaled = scaler.fit_transform(values)#转成有监督数据reframed = series_to_supervised(scaled, 1, 1)#删除不预测的列reframed.drop(reframed.columns[9:16], axis=1, inplace=True)print reframed.head() #数据准备#--------------------------values = reframed.valuesn_train_hours = 365*24 #拿一年的时间长度训练#划分训练数据和测试数据train = values[:n_train_hours, :]test = values[n_train_hours:, :]#拆分输入输出train_x, train_y = train[:, :-1], train[:, -1]test_x, test_y = test[:, :-1], test[:, -1]#reshape输入为LSTM的输入格式train_x = train_x.reshape((train_x.shape[0], 1, train_x.shape[1]))test_x = test_x.reshape((test_x.shape[0], 1, test_x.shape[1]))print 'train_x.shape, train_y.shape, test_x.shape, test_y.shape'print train_x.shape, train_y.shape, test_x.shape, test_y.shape #模型定义#-------------------------model = Sequential()model.add(LSTM(50, input_shape=(train_x.shape[1], train_x.shape[2])))model.add(Dense(1))model.compile(loss='mae', optimizer='adam') #模型训练#------------------------history = model.fit(train_x, train_y, epochs=50, batch_size=72, validation_data=(test_x, test_y), verbose=2, shuffle=False) #输出pyplot.plot(history.history['loss'], label='train')pyplot.plot(history.history['val_loss'], label='test')pyplot.legend()pyplot.show() #预测#------------------------yhat = model.predict(test_x)test_x = test_x.reshape(test_x.shape[0], test_x.shape[2])#预测数据逆缩放inv_yhat = concatenate((yhat, test_x[:, 1:]), axis=1)       inv_yhat = scaler.inverse_transform(inv_yhat)inv_yhat = inv_yhat[:, 0]#真实数据逆缩放test_y = test_y.reshape(len(test_y), 1)inv_y = concatenate((test_y, test_x[:, 1:]), axis=1)inv_y = scaler.inverse_transform(inv_y)inv_y = inv_y[:, 0]#计算rmsermse = sqrt(mean_squared_error(inv_y, inv_yhat))print 'Test RMSE:%.3f'%rmse